Deep Text Prior: Weakly Supervised Learning for Assertion Classification

Vadim Liventsev, Irina Fedulova, Dmitry Dylov

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Citations (Scopus)

    Abstract

    The success of neural networks is typically attributed to their ability to closely mimic relationships between features and labels observed in the training dataset. This, however, is only part of the answer: in addition to being fit to data, neural networks have been shown to be useful priors on the conditional distribution of labels given features and can be used as such even in the absence of trustworthy training labels. This feature of neural networks can be harnessed to train high quality models on low quality training data in tasks for which large high-quality ground truth datasets don’t exist. One of these problems is assertion classification in biomedical texts: discriminating between positive, negative and speculative statements about certain pathologies a patient may have. We present an assertion classification methodology based on recurrent neural networks, attention mechanism and two flavours of transfer learning (language modelling and heuristic annotation) that achieves state of the art results on MIMIC-CXR radiology reports.

    Original languageEnglish
    Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2019
    Subtitle of host publicationWorkshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Proceedings
    EditorsVera Kurková, Igor V. Tetko, Pavel Karpov, Fabian Theis
    PublisherSpringer Verlag
    Pages243-257
    Number of pages15
    ISBN (Print)9783030304928
    DOIs
    Publication statusPublished - 2019
    Event28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Germany
    Duration: 17 Sep 201919 Sep 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11731 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference28th International Conference on Artificial Neural Networks, ICANN 2019
    Country/TerritoryGermany
    CityMunich
    Period17/09/1919/09/19

    Keywords

    • Assertion classification
    • Biomedical texts
    • Deep learning
    • Natural language processing
    • Transfer learning
    • Weakly supervised learning

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